Although nicotine dependent cigarette smokers who quit are less likely to experience life threatening health problems and improve their quality of life, unfortunately, long-term abstinence rates are low. Models of nicotine dependence suggest the importance of multifaceted approach to understanding relapse involving biological, motivational, cognitive, and behavioral factors. While biological factors, such as genetic predisposition are directly observable, other known risk factors for relapse such as craving and impulsivity are often indirectly measured using self-report questionnaires and behavioral observations. We propose to examine predictors of smoking cessation outcome by directly measuring neural activity associated with managing cravings, decision- making about rewards, and cognitive persistence using functional magnetic resonance imaging (FMRI). Assessed behaviorally, these constructs have been shown to predict smoking cessation outcome; however, it is expected that more direct fMRI assessment of neural activity will enhance sensitivity and specificity of quantitative measurement and avoid confounds associated with subjective ratings and behavioral observation of these factors. We expect fMRI activity to yield more sensitive markers of relapse relative to behavioral and subjective measures. To accomplish this we will challenge motivational and cognitive systems while measuring activity in brain regions normally associated with these functions. When grouped by cessation outcome (i.e., lapse) we predict different brain activity in regions of interest related to these systems. Specifically, we will demonstrate that nicotine-dependent smokers who lapse early exhibit different levels of brain activity compared to smokers who exhibit prolonged abstinence. We expect that groups will also differ on behavioral ratings of craving and measures of accuracy and impulsivity during these challenges. The results of the proposed study will refine understanding the neurobehavioral correlates of known risk factors for smoking relapse, and advance development of neurobehavioral models of neural activity that predispose smokers to relapse. Neurobehavioral methods can facilitate identification of smokers at greater risk for relapse, isolate targets of neural activity for clinical interventions and facilitate delivery of specialized behavioral and pharmacologic cessation treatments. Characterization of expected brain recruitment, compensatory responses, and disorganization of active and deactivated networks provide novel information that is likely to complement our existing knowledge on the neural mechanisms related to relapse risk.